Overview

Dataset statistics

Number of variables15
Number of observations506
Missing cells120
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory59.4 KiB
Average record size in memory120.3 B

Variable types

Numeric13
Categorical2

Alerts

범죄율 is highly correlated with 주거토지비율 and 9 other fieldsHigh correlation
주거토지비율 is highly correlated with 범죄율 and 4 other fieldsHigh correlation
상업토지비율 is highly correlated with 범죄율 and 8 other fieldsHigh correlation
일산화질소 is highly correlated with 범죄율 and 8 other fieldsHigh correlation
방_개수 is highly correlated with 하위계층비율 and 2 other fieldsHigh correlation
오래된주택비율 is highly correlated with 범죄율 and 7 other fieldsHigh correlation
직업센터거리 is highly correlated with 범죄율 and 6 other fieldsHigh correlation
고속도로접근성 is highly correlated with 범죄율 and 2 other fieldsHigh correlation
재산세율 is highly correlated with 범죄율 and 8 other fieldsHigh correlation
학생교사비율 is highly correlated with 주택가격 and 1 other fieldsHigh correlation
하위계층비율 is highly correlated with 범죄율 and 8 other fieldsHigh correlation
주택가격 is highly correlated with 범죄율 and 8 other fieldsHigh correlation
주택가격등급 is highly correlated with 범죄율 and 6 other fieldsHigh correlation
범죄율 is highly correlated with 고속도로접근성 and 1 other fieldsHigh correlation
주거토지비율 is highly correlated with 상업토지비율 and 3 other fieldsHigh correlation
상업토지비율 is highly correlated with 주거토지비율 and 7 other fieldsHigh correlation
일산화질소 is highly correlated with 주거토지비율 and 6 other fieldsHigh correlation
방_개수 is highly correlated with 하위계층비율 and 2 other fieldsHigh correlation
오래된주택비율 is highly correlated with 주거토지비율 and 5 other fieldsHigh correlation
직업센터거리 is highly correlated with 주거토지비율 and 4 other fieldsHigh correlation
고속도로접근성 is highly correlated with 범죄율 and 3 other fieldsHigh correlation
재산세율 is highly correlated with 범죄율 and 7 other fieldsHigh correlation
학생교사비율 is highly correlated with 주택가격High correlation
하위계층비율 is highly correlated with 상업토지비율 and 6 other fieldsHigh correlation
주택가격 is highly correlated with 방_개수 and 3 other fieldsHigh correlation
주택가격등급 is highly correlated with 상업토지비율 and 4 other fieldsHigh correlation
범죄율 is highly correlated with 상업토지비율 and 4 other fieldsHigh correlation
주거토지비율 is highly correlated with 상업토지비율 and 1 other fieldsHigh correlation
상업토지비율 is highly correlated with 범죄율 and 3 other fieldsHigh correlation
일산화질소 is highly correlated with 범죄율 and 4 other fieldsHigh correlation
오래된주택비율 is highly correlated with 일산화질소 and 1 other fieldsHigh correlation
직업센터거리 is highly correlated with 범죄율 and 3 other fieldsHigh correlation
고속도로접근성 is highly correlated with 범죄율 and 1 other fieldsHigh correlation
재산세율 is highly correlated with 범죄율 and 1 other fieldsHigh correlation
하위계층비율 is highly correlated with 주택가격 and 1 other fieldsHigh correlation
주택가격 is highly correlated with 하위계층비율 and 1 other fieldsHigh correlation
주택가격등급 is highly correlated with 하위계층비율 and 1 other fieldsHigh correlation
범죄율 is highly correlated with 상업토지비율 and 3 other fieldsHigh correlation
주거토지비율 is highly correlated with 상업토지비율 and 7 other fieldsHigh correlation
상업토지비율 is highly correlated with 범죄율 and 9 other fieldsHigh correlation
일산화질소 is highly correlated with 범죄율 and 9 other fieldsHigh correlation
방_개수 is highly correlated with 학생교사비율 and 4 other fieldsHigh correlation
오래된주택비율 is highly correlated with 주거토지비율 and 8 other fieldsHigh correlation
직업센터거리 is highly correlated with 주거토지비율 and 9 other fieldsHigh correlation
고속도로접근성 is highly correlated with 주거토지비율 and 8 other fieldsHigh correlation
재산세율 is highly correlated with 주거토지비율 and 6 other fieldsHigh correlation
학생교사비율 is highly correlated with 주거토지비율 and 10 other fieldsHigh correlation
B is highly correlated with 범죄율 and 1 other fieldsHigh correlation
하위계층비율 is highly correlated with 방_개수 and 6 other fieldsHigh correlation
주택가격 is highly correlated with 주거토지비율 and 9 other fieldsHigh correlation
주택가격등급 is highly correlated with 범죄율 and 9 other fieldsHigh correlation
범죄율 has 20 (4.0%) missing values Missing
주거토지비율 has 20 (4.0%) missing values Missing
상업토지비율 has 20 (4.0%) missing values Missing
찰스강인근 has 20 (4.0%) missing values Missing
오래된주택비율 has 20 (4.0%) missing values Missing
하위계층비율 has 20 (4.0%) missing values Missing
주거토지비율 has 360 (71.1%) zeros Zeros

Reproduction

Analysis started2022-07-18 05:27:45.927048
Analysis finished2022-07-18 05:27:56.745286
Duration10.82 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

범죄율
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct484
Distinct (%)99.6%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean3.611873971
Minimum0.00632
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:56.798925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.00632
5-th percentile0.02739
Q10.0819
median0.253715
Q33.5602625
95-th percentile15.870875
Maximum88.9762
Range88.96988
Interquartile range (IQR)3.4783625

Descriptive statistics

Standard deviation8.72019185
Coefficient of variation (CV)2.414312326
Kurtosis36.56834838
Mean3.611873971
Median Absolute Deviation (MAD)0.218875
Skewness5.21284265
Sum1755.37075
Variance76.0417459
MonotonicityNot monotonic
2022-07-18T14:27:56.858472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.015012
 
0.4%
14.33372
 
0.4%
0.045441
 
0.2%
0.024981
 
0.2%
0.013011
 
0.2%
0.061511
 
0.2%
0.054971
 
0.2%
0.033061
 
0.2%
0.030411
 
0.2%
0.034271
 
0.2%
Other values (474)474
93.7%
(Missing)20
 
4.0%
ValueCountFrequency (%)
0.006321
0.2%
0.009061
0.2%
0.010961
0.2%
0.013011
0.2%
0.013111
0.2%
0.01361
0.2%
0.013811
0.2%
0.014321
0.2%
0.014391
0.2%
0.015012
0.4%
ValueCountFrequency (%)
88.97621
0.2%
73.53411
0.2%
67.92081
0.2%
51.13581
0.2%
45.74611
0.2%
41.52921
0.2%
38.35181
0.2%
37.66191
0.2%
28.65581
0.2%
25.94061
0.2%

주거토지비율
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct26
Distinct (%)5.3%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean11.21193416
Minimum0
Maximum100
Zeros360
Zeros (%)71.1%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:56.913625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.38887615
Coefficient of variation (CV)2.086069702
Kurtosis4.132614189
Mean11.21193416
Median Absolute Deviation (MAD)0
Skewness2.256612605
Sum5449
Variance547.0395274
MonotonicityNot monotonic
2022-07-18T14:27:56.966562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0360
71.1%
2020
 
4.0%
8014
 
2.8%
2210
 
2.0%
2510
 
2.0%
12.510
 
2.0%
406
 
1.2%
456
 
1.2%
905
 
1.0%
305
 
1.0%
Other values (16)40
 
7.9%
(Missing)20
 
4.0%
ValueCountFrequency (%)
0360
71.1%
12.510
 
2.0%
17.51
 
0.2%
181
 
0.2%
2020
 
4.0%
214
 
0.8%
2210
 
2.0%
2510
 
2.0%
282
 
0.4%
305
 
1.0%
ValueCountFrequency (%)
1001
 
0.2%
954
 
0.8%
905
 
1.0%
852
 
0.4%
82.52
 
0.4%
8014
2.8%
753
 
0.6%
703
 
0.6%
604
 
0.8%
553
 
0.6%

상업토지비율
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct76
Distinct (%)15.6%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean11.08399177
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:57.024168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.3125
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.835896499
Coefficient of variation (CV)0.6167359775
Kurtosis-1.217990915
Mean11.08399177
Median Absolute Deviation (MAD)6.32
Skewness0.3037221876
Sum5386.82
Variance46.72948094
MonotonicityNot monotonic
2022-07-18T14:27:57.081996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1127
25.1%
19.5828
 
5.5%
8.1422
 
4.3%
6.218
 
3.6%
21.8914
 
2.8%
9.912
 
2.4%
3.9712
 
2.4%
8.5611
 
2.2%
10.5911
 
2.2%
5.869
 
1.8%
Other values (66)222
43.9%
(Missing)20
 
4.0%
ValueCountFrequency (%)
0.461
 
0.2%
0.741
 
0.2%
1.211
 
0.2%
1.221
 
0.2%
1.252
0.4%
1.321
 
0.2%
1.381
 
0.2%
1.472
0.4%
1.524
0.8%
1.692
0.4%
ValueCountFrequency (%)
27.745
 
1.0%
25.656
 
1.2%
21.8914
 
2.8%
19.5828
 
5.5%
18.1127
25.1%
15.043
 
0.6%
13.924
 
0.8%
13.893
 
0.6%
12.836
 
1.2%
11.935
 
1.0%

찰스강인근
Categorical

MISSING

Distinct2
Distinct (%)0.4%
Missing20
Missing (%)4.0%
Memory size29.4 KiB
0.0
452 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1458
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0452
89.3%
1.034
 
6.7%
(Missing)20
 
4.0%

Length

2022-07-18T14:27:57.134886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-18T14:27:57.186716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0452
93.0%
1.034
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0938
64.3%
.486
33.3%
134
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number972
66.7%
Other Punctuation486
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0938
96.5%
134
 
3.5%
Other Punctuation
ValueCountFrequency (%)
.486
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1458
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0938
64.3%
.486
33.3%
134
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0938
64.3%
.486
33.3%
134
 
2.3%

일산화질소
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5546950593
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:57.235443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.40925
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.1158776757
Coefficient of variation (CV)0.2089033853
Kurtosis-0.06466713337
Mean0.5546950593
Median Absolute Deviation (MAD)0.0875
Skewness0.7293079225
Sum280.6757
Variance0.01342763572
MonotonicityNot monotonic
2022-07-18T14:27:57.295513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.53823
 
4.5%
0.71318
 
3.6%
0.43717
 
3.4%
0.87116
 
3.2%
0.62415
 
3.0%
0.48915
 
3.0%
0.69314
 
2.8%
0.60514
 
2.8%
0.7413
 
2.6%
0.54412
 
2.4%
Other values (71)349
69.0%
ValueCountFrequency (%)
0.3851
 
0.2%
0.3891
 
0.2%
0.3922
0.4%
0.3941
 
0.2%
0.3982
0.4%
0.44
0.8%
0.4013
0.6%
0.4033
0.6%
0.4043
0.6%
0.4053
0.6%
ValueCountFrequency (%)
0.87116
3.2%
0.778
1.6%
0.7413
2.6%
0.7186
 
1.2%
0.71318
3.6%
0.711
2.2%
0.69314
2.8%
0.6798
1.6%
0.6717
 
1.4%
0.6683
 
0.6%

방_개수
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct446
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.284634387
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:57.479464image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.314
Q15.8855
median6.2085
Q36.6235
95-th percentile7.5875
Maximum8.78
Range5.219
Interquartile range (IQR)0.738

Descriptive statistics

Standard deviation0.7026171434
Coefficient of variation (CV)0.1117992074
Kurtosis1.891500366
Mean6.284634387
Median Absolute Deviation (MAD)0.3455
Skewness0.4036121333
Sum3180.025
Variance0.4936708502
MonotonicityNot monotonic
2022-07-18T14:27:57.539370image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.7133
 
0.6%
6.1673
 
0.6%
6.1273
 
0.6%
6.2293
 
0.6%
6.4053
 
0.6%
6.4173
 
0.6%
6.7822
 
0.4%
6.9512
 
0.4%
6.632
 
0.4%
6.3122
 
0.4%
Other values (436)480
94.9%
ValueCountFrequency (%)
3.5611
0.2%
3.8631
0.2%
4.1382
0.4%
4.3681
0.2%
4.5191
0.2%
4.6281
0.2%
4.6521
0.2%
4.881
0.2%
4.9031
0.2%
4.9061
0.2%
ValueCountFrequency (%)
8.781
0.2%
8.7251
0.2%
8.7041
0.2%
8.3981
0.2%
8.3751
0.2%
8.3371
0.2%
8.2971
0.2%
8.2661
0.2%
8.2591
0.2%
8.2471
0.2%

오래된주택비율
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct348
Distinct (%)71.6%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean68.51851852
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:57.599194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.95
Q145.175
median76.8
Q393.975
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)48.8

Descriptive statistics

Standard deviation27.99951301
Coefficient of variation (CV)0.4086415412
Kurtosis-0.9821403245
Mean68.51851852
Median Absolute Deviation (MAD)20.15
Skewness-0.5824700575
Sum33300
Variance783.9727285
MonotonicityNot monotonic
2022-07-18T14:27:57.659239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10042
 
8.3%
97.94
 
0.8%
87.94
 
0.8%
98.84
 
0.8%
964
 
0.8%
95.44
 
0.8%
76.53
 
0.6%
973
 
0.6%
96.23
 
0.6%
32.23
 
0.6%
Other values (338)412
81.4%
(Missing)20
 
4.0%
ValueCountFrequency (%)
2.91
0.2%
6.21
0.2%
6.51
0.2%
6.62
0.4%
6.81
0.2%
7.82
0.4%
8.41
0.2%
8.91
0.2%
9.81
0.2%
101
0.2%
ValueCountFrequency (%)
10042
8.3%
99.31
 
0.2%
99.11
 
0.2%
98.93
 
0.6%
98.84
 
0.8%
98.71
 
0.2%
98.51
 
0.2%
98.42
 
0.4%
98.32
 
0.4%
98.22
 
0.4%

직업센터거리
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct412
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.795042688
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:57.719559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.461975
Q12.100175
median3.20745
Q35.188425
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.08825

Descriptive statistics

Standard deviation2.105710127
Coefficient of variation (CV)0.5548580872
Kurtosis0.4879411222
Mean3.795042688
Median Absolute Deviation (MAD)1.29115
Skewness1.011780579
Sum1920.2916
Variance4.434015137
MonotonicityNot monotonic
2022-07-18T14:27:57.778297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.49525
 
1.0%
5.72094
 
0.8%
5.28734
 
0.8%
6.81474
 
0.8%
5.40074
 
0.8%
6.33613
 
0.6%
3.94543
 
0.6%
6.4983
 
0.6%
4.72113
 
0.6%
4.81223
 
0.6%
Other values (402)470
92.9%
ValueCountFrequency (%)
1.12961
0.2%
1.1371
0.2%
1.16911
0.2%
1.17421
0.2%
1.17811
0.2%
1.20241
0.2%
1.28521
0.2%
1.31631
0.2%
1.32161
0.2%
1.33251
0.2%
ValueCountFrequency (%)
12.12651
0.2%
10.71032
0.4%
10.58572
0.4%
9.22291
0.2%
9.22032
0.4%
9.18761
0.2%
9.08921
0.2%
8.90672
0.4%
8.79212
0.4%
8.69661
0.2%

고속도로접근성
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.549407115
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:57.827471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.707259384
Coefficient of variation (CV)0.9118115166
Kurtosis-0.8672319936
Mean9.549407115
Median Absolute Deviation (MAD)2
Skewness1.004814648
Sum4832
Variance75.81636598
MonotonicityNot monotonic
2022-07-18T14:27:57.874717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24132
26.1%
5115
22.7%
4110
21.7%
338
 
7.5%
626
 
5.1%
224
 
4.7%
824
 
4.7%
120
 
4.0%
717
 
3.4%
ValueCountFrequency (%)
120
 
4.0%
224
 
4.7%
338
 
7.5%
4110
21.7%
5115
22.7%
626
 
5.1%
717
 
3.4%
824
 
4.7%
24132
26.1%
ValueCountFrequency (%)
24132
26.1%
824
 
4.7%
717
 
3.4%
626
 
5.1%
5115
22.7%
4110
21.7%
338
 
7.5%
224
 
4.7%
120
 
4.0%

재산세율
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct66
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.2371542
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:57.929661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.5371161
Coefficient of variation (CV)0.4128411987
Kurtosis-1.142407992
Mean408.2371542
Median Absolute Deviation (MAD)73
Skewness0.6699559418
Sum206568
Variance28404.75949
MonotonicityNot monotonic
2022-07-18T14:27:57.990278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666132
26.1%
30740
 
7.9%
40330
 
5.9%
43715
 
3.0%
30414
 
2.8%
26412
 
2.4%
39812
 
2.4%
38411
 
2.2%
27711
 
2.2%
22410
 
2.0%
Other values (56)219
43.3%
ValueCountFrequency (%)
1871
 
0.2%
1887
1.4%
1938
1.6%
1981
 
0.2%
2165
1.0%
2227
1.4%
2235
1.0%
22410
2.0%
2261
 
0.2%
2339
1.8%
ValueCountFrequency (%)
7115
 
1.0%
666132
26.1%
4691
 
0.2%
43715
 
3.0%
4329
 
1.8%
4303
 
0.6%
4221
 
0.2%
4112
 
0.4%
40330
 
5.9%
4022
 
0.4%

학생교사비율
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.4555336
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:58.049543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.05
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.164945524
Coefficient of variation (CV)0.1173060379
Kurtosis-0.2850913833
Mean18.4555336
Median Absolute Deviation (MAD)1.15
Skewness-0.8023249269
Sum9338.5
Variance4.686989121
MonotonicityNot monotonic
2022-07-18T14:27:58.107888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2140
27.7%
14.734
 
6.7%
2127
 
5.3%
17.823
 
4.5%
19.219
 
3.8%
17.418
 
3.6%
18.617
 
3.4%
19.117
 
3.4%
18.416
 
3.2%
16.616
 
3.2%
Other values (36)179
35.4%
ValueCountFrequency (%)
12.63
 
0.6%
1312
 
2.4%
13.61
 
0.2%
14.41
 
0.2%
14.734
6.7%
14.83
 
0.6%
14.94
 
0.8%
15.11
 
0.2%
15.213
 
2.6%
15.33
 
0.6%
ValueCountFrequency (%)
222
 
0.4%
21.215
 
3.0%
21.11
 
0.2%
2127
 
5.3%
20.911
 
2.2%
20.2140
27.7%
20.15
 
1.0%
19.78
 
1.6%
19.68
 
1.6%
19.219
 
3.8%

B
Real number (ℝ≥0)

HIGH CORRELATION

Distinct357
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.6740316
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:58.167653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.59
Q1375.3775
median391.44
Q3396.225
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.8475

Descriptive statistics

Standard deviation91.29486438
Coefficient of variation (CV)0.255961624
Kurtosis7.226817549
Mean356.6740316
Median Absolute Deviation (MAD)5.46
Skewness-2.890373712
Sum180477.06
Variance8334.752263
MonotonicityNot monotonic
2022-07-18T14:27:58.228024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9121
 
23.9%
393.743
 
0.6%
395.243
 
0.6%
376.142
 
0.4%
394.722
 
0.4%
395.632
 
0.4%
392.82
 
0.4%
395.562
 
0.4%
390.942
 
0.4%
393.682
 
0.4%
Other values (347)365
72.1%
ValueCountFrequency (%)
0.321
0.2%
2.521
0.2%
2.61
0.2%
3.51
0.2%
3.651
0.2%
6.681
0.2%
7.681
0.2%
9.321
0.2%
10.481
0.2%
16.451
0.2%
ValueCountFrequency (%)
396.9121
23.9%
396.421
 
0.2%
396.331
 
0.2%
396.31
 
0.2%
396.281
 
0.2%
396.241
 
0.2%
396.231
 
0.2%
396.212
 
0.4%
396.141
 
0.2%
396.062
 
0.4%

하위계층비율
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct438
Distinct (%)90.1%
Missing20
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean12.7154321
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:58.291836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.7075
Q17.125
median11.43
Q316.955
95-th percentile27.15
Maximum37.97
Range36.24
Interquartile range (IQR)9.83

Descriptive statistics

Standard deviation7.155870816
Coefficient of variation (CV)0.5627705579
Kurtosis0.5186825176
Mean12.7154321
Median Absolute Deviation (MAD)4.795
Skewness0.908891837
Sum6179.7
Variance51.20648713
MonotonicityNot monotonic
2022-07-18T14:27:58.350739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.793
 
0.6%
6.363
 
0.6%
8.053
 
0.6%
14.13
 
0.6%
18.133
 
0.6%
30.812
 
0.4%
4.592
 
0.4%
7.392
 
0.4%
12.672
 
0.4%
5.292
 
0.4%
Other values (428)461
91.1%
(Missing)20
 
4.0%
ValueCountFrequency (%)
1.731
0.2%
1.921
0.2%
1.981
0.2%
2.471
0.2%
2.871
0.2%
2.881
0.2%
2.941
0.2%
2.961
0.2%
2.971
0.2%
2.981
0.2%
ValueCountFrequency (%)
37.971
0.2%
36.981
0.2%
34.771
0.2%
34.411
0.2%
34.371
0.2%
34.021
0.2%
31.991
0.2%
30.812
0.4%
30.631
0.2%
30.621
0.2%

주택가격
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.53280632
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2022-07-18T14:27:58.411174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117.025
median21.2
Q325
95-th percentile43.4
Maximum50
Range45
Interquartile range (IQR)7.975

Descriptive statistics

Standard deviation9.197104087
Coefficient of variation (CV)0.408165053
Kurtosis1.495196944
Mean22.53280632
Median Absolute Deviation (MAD)4
Skewness1.108098408
Sum11401.6
Variance84.58672359
MonotonicityNot monotonic
2022-07-18T14:27:58.472481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5016
 
3.2%
258
 
1.6%
227
 
1.4%
21.77
 
1.4%
23.17
 
1.4%
19.46
 
1.2%
20.66
 
1.2%
13.85
 
1.0%
21.45
 
1.0%
20.15
 
1.0%
Other values (219)434
85.8%
ValueCountFrequency (%)
52
0.4%
5.61
 
0.2%
6.31
 
0.2%
72
0.4%
7.23
0.6%
7.41
 
0.2%
7.51
 
0.2%
8.11
 
0.2%
8.32
0.4%
8.42
0.4%
ValueCountFrequency (%)
5016
3.2%
48.81
 
0.2%
48.51
 
0.2%
48.31
 
0.2%
46.71
 
0.2%
461
 
0.2%
45.41
 
0.2%
44.81
 
0.2%
441
 
0.2%
43.81
 
0.2%

주택가격등급
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size28.8 KiB
1
325 
0
97 
2
84 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters506
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1325
64.2%
097
 
19.2%
284
 
16.6%

Length

2022-07-18T14:27:58.525278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-07-18T14:27:58.570477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1325
64.2%
097
 
19.2%
284
 
16.6%

Most occurring characters

ValueCountFrequency (%)
1325
64.2%
097
 
19.2%
284
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number506
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1325
64.2%
097
 
19.2%
284
 
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common506
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1325
64.2%
097
 
19.2%
284
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1325
64.2%
097
 
19.2%
284
 
16.6%

Interactions

2022-07-18T14:27:55.734550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.288420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.961726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.704993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.377153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.111778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.786604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.458232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.213282image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.889351image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.563254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.228698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.035109image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.784695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.347012image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.012233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.755615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.427053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.162674image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.837570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.508676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.263846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.939840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.613094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.281711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.092354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.835876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.398094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.063965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.806816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.478235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.214185image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.888920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.559957image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.315298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.991392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.663894image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.335605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.146538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.887914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.450242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.116971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.858027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.529308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.265687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.940223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.611658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.366381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.042665image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.714578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.389832image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.200879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.940027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.501041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.168345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.909001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.579779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.316578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.991289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.662885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.417323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.093520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.765417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.443213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.254578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.993532image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.551604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.219850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.960504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.630677image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.370042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.043059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.714439image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.470221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.145170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.816268image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.597143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.307944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:56.045042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.602264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.273309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.011942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.681653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.421425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.094628image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.765907image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.521767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.196563image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.867266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.652224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.360962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:56.096113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.655157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.391106image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.063511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.732514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.472712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.146032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.817594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.576369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.248302image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.917992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.706744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.413964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:56.146916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.705866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.442867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.114729image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.785449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.524004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.197672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.869883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.628730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.300040image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.969017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.760767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.468009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:56.198054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.756715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.494508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.167970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.837181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.575751image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.249207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.921661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.680155image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.351597image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.022702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.814458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.521029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:56.248360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.806546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.545471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.219725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.887732image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.626535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.300260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.972995image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.730935image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.405378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.073030image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.867711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.573284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:56.301490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.859709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.601007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.273477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.941078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.680436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.353937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.109479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.784613image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.459536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.126269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.923936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.628187image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:56.353594image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:47.911737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:48.654078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:49.326307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.061275image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:50.733446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:51.406981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.162427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:52.838416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:53.512394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.178521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:54.980484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-07-18T14:27:55.682450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-07-18T14:27:58.645211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-18T14:27:58.749518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-18T14:27:58.856941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-18T14:27:58.953074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-18T14:27:59.039785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-18T14:27:56.475572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-18T14:27:56.561031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-07-18T14:27:56.665052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-07-18T14:27:56.721090image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

범죄율주거토지비율상업토지비율찰스강인근일산화질소방_개수오래된주택비율직업센터거리고속도로접근성재산세율학생교사비율B하위계층비율주택가격주택가격등급
00.0063218.02.310.00.5386.57565.24.0900129615.3396.904.9824.01
10.027310.07.070.00.4696.42178.94.9671224217.8396.909.1421.61
20.027290.07.070.00.4697.18561.14.9671224217.8392.834.0334.72
30.032370.02.180.00.4586.99845.86.0622322218.7394.632.9433.42
40.069050.02.180.00.4587.14754.26.0622322218.7396.90NaN36.22
50.029850.02.180.00.4586.43058.76.0622322218.7394.125.2128.71
60.0882912.57.87NaN0.5246.01266.65.5605531115.2395.6012.4322.91
70.1445512.57.870.00.5246.17296.15.9505531115.2396.9019.1527.11
80.2112412.57.870.00.5245.631100.06.0821531115.2386.6329.9316.51
90.1700412.57.87NaN0.5246.00485.96.5921531115.2386.7117.1018.91

Last rows

범죄율주거토지비율상업토지비율찰스강인근일산화질소방_개수오래된주택비율직업센터거리고속도로접근성재산세율학생교사비율B하위계층비율주택가격주택가격등급
4960.289600.09.690.00.5855.39072.92.7986639119.2396.9021.1419.71
4970.268380.09.690.00.5855.79470.62.8927639119.2396.9014.1018.31
4980.239120.09.690.00.5856.01965.32.4091639119.2396.9012.9221.21
4990.177830.09.690.00.5855.56973.52.3999639119.2395.7715.1017.51
5000.224380.09.690.00.5856.02779.72.4982639119.2396.9014.3316.81
5010.062630.011.930.00.5736.59369.12.4786127321.0391.99NaN22.41
5020.045270.011.930.00.5736.12076.72.2875127321.0396.909.0820.61
5030.060760.011.930.00.5736.97691.02.1675127321.0396.905.6423.91
5040.109590.011.930.00.5736.79489.32.3889127321.0393.456.4822.01
5050.047410.011.930.00.5736.030NaN2.5050127321.0396.907.8811.90